This research aims to transform the use and interpretation of social network models for infectious disease investigation, control, and research. Epidemics such as SARS or H1N1 influenza are transmitted largely through social routes, yet we often lack the tools to identify and interrupt this transmission. The use of network models of disease transmission has expanded broadly to include contact investigation, vaccination policy, epidemic models, and disease surveillance. However, these networks present cognitive challenges to users and are often static and incomprehensible. While many tools exist to visualize and analyze network data, these methods have not been broadly evaluated for their validity, consistency, and contribution to health outcomes. Inconsistent integration with genomic, clinical, and geographic data further limits these tools to niche applications. We propose that through interactive user interfaces, integration with clinical, genomic, and geographic data will increase the accessibility of network analytic techniques. The goal of this research is to increase the validity, interpretability, and utility of network analyse so that this synthesized information can be effectively incorporated into routine infectious disease control. To accomplish this, we will 1) develop new methods to integrate diverse epidemiologic data (GIS, genomic, and clinical) into social network analyses, 2) extend the Outbreak Investigator analysis software to visualize this integrated data, and 3) evaluate the utility of network visualization techniques for infectious disease control utilizing this synthesized data. Using real and simulated outbreaks from diseases such as tuberculosis, influenza, and pertussis, our methods will assess the impact of interactive visualization, missing data, joint displays of social data with clinical/geographic/genomic information, and dynamic network displays. Outcome metrics measured will include the efficiency of outbreak detection (sensitivity, specificity and timeliness), and software usability measures. These studies will control for variables such as outbreak size, degree of missing information, and individual user effects. Through this systematic approach, the research aims to extend the reach and impact of network models on human health. The end result of this research will be to 1) provide a broader and deeper evidence base for use of network analysis in a key biomedical setting, 2) improve decision-making with diverse and complex data for infectious disease control, and 3) development of new visualization algorithms applicable to a broad array of biomedical data. This research will advance the field of informatics through the development and validation of innovative data integration, interactive visualization, and collaborative technologies for biomedical data.